The increasing presence of drones seen on the battlefields in modern conflicts poses new threats to manned military aircraft or rotorcraft.In order to assess this potential threat,this manuscript first summarizes all ...The increasing presence of drones seen on the battlefields in modern conflicts poses new threats to manned military aircraft or rotorcraft.In order to assess this potential threat,this manuscript first summarizes all confirmed and suspected collisions between drones and aerostructures and the damage resulting from these collisions.Furthermore,this manuscript reviews experimental and numerical investigations on collision of drones with aerostructures.Additionally,some light is shed onto current regulation for drone operations intended to avoid collisions between drones and aircraft.Whilst these regulatory measures can prevent commercial aircraft to collide with drones,the authors believe that there is an inherent threat for civil and military rotorcraft due to their structural design and the fact that it is not possible to completely separate the airspace between drone operations and rotorcraft operations,in particular in the context of rescue missions in an urban or hostile environment.Furthermore,the stealth capability of 5th generation fighters may be compromised by damage suffered from collision with drones.展开更多
Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.Ho...Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.However,observing sleeping sites at night is challenging,especially for species sensitive to human disturbance.Thermal infrared imaging(TIR)with drones is increasingly used for detecting and counting primates,yet it has not been utilized to investigate ecological strategies.This study investigates the sleeping site selection of the Critically Endangered black-shanked douc langur(Pygathrix nigripes)in Cát Tiên National Park,Vietnam.Our aim is to assess the feasibility of using a TIR drone to test sleeping site selection strategies in non-nesting primates,specifically examining hypotheses related to predation avoidance and food proximity.Between January and April 2023,we conducted 120 drone flights along 22 transects(~1-km long)and identified 114 sleeping sites via thermal imaging.We established 116 forest structure plots along 29 transects in non-selected sites and 65 plots within douc langur sleeping sites.Our observations reveal that douc langurs selected tall and large trees that may provide protection against predators.Additionally,they selected sleeping sites with increased access to food,such as Afzelia xylocarpa,which serves as a preferred food source during the dry season.These results highlight the effective use of TIR drones for studying douc langur sleeping site selection with minimal disturbance.Besides offering valuable insights into habitat selection and behavioral ecology for conservation,TIR drones hold great promise for the noninvasive and long-term monitoring of large-bodied arboreal species.展开更多
As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in mult...As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in multi-trip and multi-drop(VRP-mmD).The problem aims at making schedules for the trucks and drones such that the total travel time is minimized.This paper formulate the problem with a mixed integer program-ming model and propose a two-phase algorithm,i.e.,a parallel route construction heuristic(PRCH)for the first phase and an adaptive neighbor searching heuristic(ANSH)for the second phase.The PRCH generates an initial solution by con-currently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase.Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase.Numerical tests on some benchmark data are conducted to verify the performance of the algorithm.The results show that the proposed algorithm can found better solu-tions than some state-of-the-art methods for all instances.More-over,an extensive analysis highlights the stability of the pro-posed algorithm.展开更多
To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention predicti...To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio...Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.展开更多
Paying an additional RMB 2 could have your next milk tea delivered by drone to your balcony in just five minutes.This small fee represents the vast potential of the trillion-yuan lowaltitude economy.
This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized...This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring.展开更多
The paper presents the digital image objects detection and recognition system using artificial neural networks and drones. It contains description based on the example of person identification system where face is the...The paper presents the digital image objects detection and recognition system using artificial neural networks and drones. It contains description based on the example of person identification system where face is the key of object processing. It describes the structure of this system and components of the learning sub-system as well as the processing sub-system （detection, recognition）. It consists of the description and examples of learning and processing algorithms and applied technologies. The results of calculations of efficiency and speed of each algorithm are presented in the table and appropriate characteristics. The article also describes the possibilities of further system developments.展开更多
Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam co...Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam composed of carbon fiber and polymethacrylimide(PMI)foam is conducive to realize the lightweight of structure.In this study,a two-stage optimization design methodology for sandwich box beam is proposed.This methodology is primarily based on a low-order analytical method for evaluating stress/deflection and the linear buckling analysis method combined with experimental correction factor for predicting the buckling eigenvalues.Subsequently,a case study was conducted using an 18-m wingspan solar drone,where the results of mechanical test verified the optimization results.For validating the use of sandwich box beam in solar drones of other scales,additional analysis was conducted based on three aspects:(A)effects of stiffness and stability constraints on the design of sandwich box beam;(B)crucial role of the weight of foam inter layer and application scope of sandwich box beam;(C)best method to improve the buckling eigenvalue of sandwich box beam.Overall,the methodology and general rules presented in this paper can support the design of light wing beam for solar drones.展开更多
As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current s...As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.展开更多
The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agricultu...The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.展开更多
Recently, drones have found applicability in a variety of study fields, one of these being forestry, where an increasing interest is given to this segment of technology, especially due to the high-resolution data that...Recently, drones have found applicability in a variety of study fields, one of these being forestry, where an increasing interest is given to this segment of technology, especially due to the high-resolution data that can be collected flexibly in a short time and at a relatively low price. Also, drones have an important role in filling the gaps of common data collected using manned aircraft or satellite remote sensing, while having many advantages both in research and in various practical applications particularly in forestry as well as in land use in general. This paper aims to briefly describe the different approaches of applications of UAVs （Unmanned Aircraft Vehicles） in forestry, such as forest mapping, forest management planning, canopy height model creation or mapping forest gaps. These approaches have great potential in the near future applications and their quick implementation in a variety of situations is desirable for the sustainable management of forests.展开更多
In order to achieve the specific goal of a smart grid,the concept of electricity Internet of Things(eloT)has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asse...In order to achieve the specific goal of a smart grid,the concept of electricity Internet of Things(eloT)has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asset utilization.The long power transmission line and the complex field operation environment urge the introduction of drones into the eloT for fast power transmission line inspection,data collection from sensors for further big data analysis,adaptive control of power line voltage,etc.Additionally,drones can also act as a central communication control or relay point to serve the data exchange among sensors,drones and power transmission line maintenance personnel in the scenario where the conventional mobile communication service is not available.However,the fast mobility of drones may affect the signal transmission and position estimation performance,which may further deteriorate the networking performance.In order to solve this problem,a mobility compensation method is proposed,which includes the steps of frequency offset estimation and relative velocity calculation.Through the Monte Carlo simulations,the proposed algorithm shows favorable gains compared with the conventional ones.展开更多
This review paper focuses on cooperative robotic arms with mobile or drone bases performing cooperative tasks. This is because cooperative robots are often used as risk-reduction tools to human life. For example, they...This review paper focuses on cooperative robotic arms with mobile or drone bases performing cooperative tasks. This is because cooperative robots are often used as risk-reduction tools to human life. For example, they are used to explore dangerous places such as minefields and disarm explosives. Drones can be used to perform tasks such as aerial photography, military and defense missions,agricultural surveys, etc. The bases of the cooperative robotic arms can be stationary, mobile(ground), or drones. Cooperative manipulators allow faster performance of assigned tasks because of the available "extra hand". Furthermore, a mobile base increases the reachable ground workspace of cooperative manipulators while a drone base drastically increases this workspace to include the aerial space.The papers in this review are chosen to extensively cover a wide variety of cooperative manipulation tasks and industries that use them.In cooperative manipulation, avoiding self-collision is one of the most important tasks to be performed. In addition, path planning and formation control can be challenging because of the increased number of components to be coordinated.展开更多
With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually ...With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.展开更多
The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions f...The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments.In this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks.The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as buildings.The algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone swarm.First,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the swarm.Second,the algorithm identifies observers within the swarm based on the current orientation.To further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy logic.This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular velocity.The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle avoidance.The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations.The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.展开更多
Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performa...Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.展开更多
The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT...The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst drones.While drones are naturally mobile,it undergoes frequent topological changes.Such alterations in the topology cause route election,stability,and scalability problems in IoD.Encryption is considered as an effective method to transmit the images in IoD environment.The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)environment.The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones.The presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction technique.In addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment.This process enables the secure transmission of images to the ground station.In order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different aspects.The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.展开更多
The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible...The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time applications.As several applications comprise IoD in real time environment,significant interest has been received by research communications.Since IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD environment.This article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD environment.The major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD environment.To do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible format.In addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate features.Moreover,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of FFA.The performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.展开更多
文摘The increasing presence of drones seen on the battlefields in modern conflicts poses new threats to manned military aircraft or rotorcraft.In order to assess this potential threat,this manuscript first summarizes all confirmed and suspected collisions between drones and aerostructures and the damage resulting from these collisions.Furthermore,this manuscript reviews experimental and numerical investigations on collision of drones with aerostructures.Additionally,some light is shed onto current regulation for drone operations intended to avoid collisions between drones and aircraft.Whilst these regulatory measures can prevent commercial aircraft to collide with drones,the authors believe that there is an inherent threat for civil and military rotorcraft due to their structural design and the fact that it is not possible to completely separate the airspace between drone operations and rotorcraft operations,in particular in the context of rescue missions in an urban or hostile environment.Furthermore,the stealth capability of 5th generation fighters may be compromised by damage suffered from collision with drones.
基金financial support of the Belgian National Fund for Scientific Research(FNRS)the Duesberg Foundation,and the University of Liège.
文摘Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.However,observing sleeping sites at night is challenging,especially for species sensitive to human disturbance.Thermal infrared imaging(TIR)with drones is increasingly used for detecting and counting primates,yet it has not been utilized to investigate ecological strategies.This study investigates the sleeping site selection of the Critically Endangered black-shanked douc langur(Pygathrix nigripes)in Cát Tiên National Park,Vietnam.Our aim is to assess the feasibility of using a TIR drone to test sleeping site selection strategies in non-nesting primates,specifically examining hypotheses related to predation avoidance and food proximity.Between January and April 2023,we conducted 120 drone flights along 22 transects(~1-km long)and identified 114 sleeping sites via thermal imaging.We established 116 forest structure plots along 29 transects in non-selected sites and 65 plots within douc langur sleeping sites.Our observations reveal that douc langurs selected tall and large trees that may provide protection against predators.Additionally,they selected sleeping sites with increased access to food,such as Afzelia xylocarpa,which serves as a preferred food source during the dry season.These results highlight the effective use of TIR drones for studying douc langur sleeping site selection with minimal disturbance.Besides offering valuable insights into habitat selection and behavioral ecology for conservation,TIR drones hold great promise for the noninvasive and long-term monitoring of large-bodied arboreal species.
文摘As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in multi-trip and multi-drop(VRP-mmD).The problem aims at making schedules for the trucks and drones such that the total travel time is minimized.This paper formulate the problem with a mixed integer program-ming model and propose a two-phase algorithm,i.e.,a parallel route construction heuristic(PRCH)for the first phase and an adaptive neighbor searching heuristic(ANSH)for the second phase.The PRCH generates an initial solution by con-currently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase.Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase.Numerical tests on some benchmark data are conducted to verify the performance of the algorithm.The results show that the proposed algorithm can found better solu-tions than some state-of-the-art methods for all instances.More-over,an extensive analysis highlights the stability of the pro-posed algorithm.
文摘To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金supported by the National Natural Science Foundation of China(No.U2433214)。
文摘Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.
文摘Paying an additional RMB 2 could have your next milk tea delivered by drone to your balcony in just five minutes.This small fee represents the vast potential of the trillion-yuan lowaltitude economy.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)(IITP-2025-RS-2022-00156326,50)grant funded by theKorea government(Ministry of Science and ICT)supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring.
文摘The paper presents the digital image objects detection and recognition system using artificial neural networks and drones. It contains description based on the example of person identification system where face is the key of object processing. It describes the structure of this system and components of the learning sub-system as well as the processing sub-system （detection, recognition）. It consists of the description and examples of learning and processing algorithms and applied technologies. The results of calculations of efficiency and speed of each algorithm are presented in the table and appropriate characteristics. The article also describes the possibilities of further system developments.
文摘Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam composed of carbon fiber and polymethacrylimide(PMI)foam is conducive to realize the lightweight of structure.In this study,a two-stage optimization design methodology for sandwich box beam is proposed.This methodology is primarily based on a low-order analytical method for evaluating stress/deflection and the linear buckling analysis method combined with experimental correction factor for predicting the buckling eigenvalues.Subsequently,a case study was conducted using an 18-m wingspan solar drone,where the results of mechanical test verified the optimization results.For validating the use of sandwich box beam in solar drones of other scales,additional analysis was conducted based on three aspects:(A)effects of stiffness and stability constraints on the design of sandwich box beam;(B)crucial role of the weight of foam inter layer and application scope of sandwich box beam;(C)best method to improve the buckling eigenvalue of sandwich box beam.Overall,the methodology and general rules presented in this paper can support the design of light wing beam for solar drones.
基金co-supported by the National Natural Science Foundation of China (Nos. U1933130,71731001,1433203,U1533119)the Research Project of Chinese Academy of Sciences (No. ZDRW-KT-2020-21-2)。
文摘As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.
基金This project was supported financially by Institution Fund projects under Grant No.(IFPIP-1266-611-1442).
文摘The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.
文摘Recently, drones have found applicability in a variety of study fields, one of these being forestry, where an increasing interest is given to this segment of technology, especially due to the high-resolution data that can be collected flexibly in a short time and at a relatively low price. Also, drones have an important role in filling the gaps of common data collected using manned aircraft or satellite remote sensing, while having many advantages both in research and in various practical applications particularly in forestry as well as in land use in general. This paper aims to briefly describe the different approaches of applications of UAVs （Unmanned Aircraft Vehicles） in forestry, such as forest mapping, forest management planning, canopy height model creation or mapping forest gaps. These approaches have great potential in the near future applications and their quick implementation in a variety of situations is desirable for the sustainable management of forests.
文摘In order to achieve the specific goal of a smart grid,the concept of electricity Internet of Things(eloT)has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asset utilization.The long power transmission line and the complex field operation environment urge the introduction of drones into the eloT for fast power transmission line inspection,data collection from sensors for further big data analysis,adaptive control of power line voltage,etc.Additionally,drones can also act as a central communication control or relay point to serve the data exchange among sensors,drones and power transmission line maintenance personnel in the scenario where the conventional mobile communication service is not available.However,the fast mobility of drones may affect the signal transmission and position estimation performance,which may further deteriorate the networking performance.In order to solve this problem,a mobility compensation method is proposed,which includes the steps of frequency offset estimation and relative velocity calculation.Through the Monte Carlo simulations,the proposed algorithm shows favorable gains compared with the conventional ones.
基金by Botswana International University of Science and Technology(BIUST)Drones Project(No.P00015).
文摘This review paper focuses on cooperative robotic arms with mobile or drone bases performing cooperative tasks. This is because cooperative robots are often used as risk-reduction tools to human life. For example, they are used to explore dangerous places such as minefields and disarm explosives. Drones can be used to perform tasks such as aerial photography, military and defense missions,agricultural surveys, etc. The bases of the cooperative robotic arms can be stationary, mobile(ground), or drones. Cooperative manipulators allow faster performance of assigned tasks because of the available "extra hand". Furthermore, a mobile base increases the reachable ground workspace of cooperative manipulators while a drone base drastically increases this workspace to include the aerial space.The papers in this review are chosen to extensively cover a wide variety of cooperative manipulation tasks and industries that use them.In cooperative manipulation, avoiding self-collision is one of the most important tasks to be performed. In addition, path planning and formation control can be challenging because of the increased number of components to be coordinated.
基金supported by the National Natural Science Foundation of China (62101603)the Shenzhen Science and Technology Program(KQTD20190929172704911)+3 种基金the Aeronautical Science Foundation of China (2019200M1001)the National Nature Science Foundation of Guangdong (2021A1515011979)the Guangdong Key Laboratory of Advanced IntelliSense Technology (2019B121203006)the Pearl R iver Talent Recruitment Program (2019ZT08X751)。
文摘With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.
文摘The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments.In this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks.The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as buildings.The algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone swarm.First,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the swarm.Second,the algorithm identifies observers within the swarm based on the current orientation.To further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy logic.This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular velocity.The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle avoidance.The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations.The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.
文摘Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project Under Grant Number(46/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238),Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR25.
文摘The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst drones.While drones are naturally mobile,it undergoes frequent topological changes.Such alterations in the topology cause route election,stability,and scalability problems in IoD.Encryption is considered as an effective method to transmit the images in IoD environment.The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)environment.The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones.The presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction technique.In addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment.This process enables the secure transmission of images to the ground station.In order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different aspects.The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4210118DSR18.
文摘The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time applications.As several applications comprise IoD in real time environment,significant interest has been received by research communications.Since IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD environment.This article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD environment.The major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD environment.To do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible format.In addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate features.Moreover,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of FFA.The performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.
